Automated Manufacturing Robot Fault Diagnosis in Real Time Using Convolutional Neural Networks

This study introduced a novel real-time Fault Diagnosis Model (FDM) in manufacturing robots by integrating Depthwise Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The objective is to design a model that can handle the complex high-dimensional sensor data that arrives out of complex, non-linear systems for effective FDM. The work introduced a Feature Extraction (FE) model based on Monte Carlo Filtering (MCF). The work integrates a Depthwise CNN with BiLSTM (DC-BiLSTM) for diagnosis. The integration helps to reduce the computational need and, at the same time, preserve the feature representation. The model was experimented against other models, such as CNN, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Feed-Forward Neural Networks (FFNN), using a fault dataset sourced from a simulated environment. The results have shown that the proposed model fared well in terms of accuracy, precision, recall, and F1 score against all compared models. The results have judged the proposed model’s applicability in the field of fault diagnosis, which could effectively predict mishaps in advance, thereby helping with efficient maintenance and ensuring continuous productivity.